Neurological Sciences

, Volume 39, Issue 5, pp 847–850 | Cite as

An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls

  • George D. Vavougios
  • Triantafyllos Doskas
  • Kostas Konstantopoulos
Original Article


Dysarthrophonia is a predominant symptom in many neurological diseases, affecting the quality of life of the patients. In this study, we produced a discriminant function equation that can differentiate MS patients from healthy controls, using electroglottographic variables not analyzed in a previous study. We applied stepwise linear discriminant function analysis in order to produce a function and score derived from electroglottographic variables extracted from a previous study. The derived discriminant function’s statistical significance was determined via Wilk’s λ test (and the associated p value). Finally, a 2 × 2 confusion matrix was used to determine the function’s predictive accuracy, whereas the cross-validated predictive accuracy is estimated via the “leave-one-out” classification process. Discriminant function analysis (DFA) was used to create a linear function of continuous predictors. DFA produced the following model (Wilk’s λ = 0.043, χ2 = 388.588, p < 0.0001, Tables 3 and 4): D (MS vs controls) = 0.728*DQx1 mean monologue + 0.325*CQx monologue + 0.298*DFx1 90% range monologue + 0.443*DQx1 90% range reading − 1.490*DQx1 90% range monologue. The derived discriminant score (S1) was used subsequently in order to form the coordinates of a ROC curve. Thus, a cutoff score of − 0.788 for S1 corresponded to a perfect classification (100% sensitivity and 100% specificity, p = 1.67e−22). Consistent with previous findings, electroglottographic evaluation represents an easy to implement and potentially important assessment in MS patients, achieving adequate classification accuracy. Further evaluation is needed to determine its use as a biomarker.


Electroglottography Linear discriminant function analysis Multiple sclerosis 


  1. 1.
    Beliavsky A, Perry JJ, Dowlatshahi D, Wasserman J, Sivilotti MLA, Sutherland J, Worster A, Émond M, Stotts G, Jin AY, Oczkowski WJ, Sahlas DJ, Murray HE, MacKey A, Verreault S, Wells GA, Stiell IG, Sharma M (2014) Acute isolated dysarthria is associated with a high risk of stroke. Cerebrovasc Dis Extra 4(2):182–185. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Urban PP, Marx J, Hunsche S, Gawehn J, Vucurevic G, Wicht S, Massinger C, Stoeter P, Hopf HC (2003) Cerebellar speech representation: lesion topography in dysarthria as derived from cerebellar ischemia and functional magnetic resonance imaging. Arch Neurol 60(7):965–972. CrossRefPubMedGoogle Scholar
  3. 3.
    Urban PP, Rolke R, Wicht S, Keilmann A, Stoeter P, Hopf HC, Dieterich M (2006) Left-hemispheric dominance for articulation: a prospective study on acute ischaemic dysarthria at different localizations. Brain 129(3):767–777. CrossRefPubMedGoogle Scholar
  4. 4.
    Konstantopoulos K, Vikelis M, Seikel JA, Mitsikostas DD (2010) The existence of phonatory instability in multiple sclerosis: an acoustic and electroglottographic study. Neurol Sci 31(3):259–268. CrossRefPubMedGoogle Scholar
  5. 5.
    Midi I, Dogan M, Koseoglu M, Can G, Sehitoglu MA, Gunal DI (2008) Voice abnormalities and their relation with motor dysfunction in Parkinson’s disease. Acta Neurol Scand 117(1):26–34. PubMedGoogle Scholar
  6. 6.
    Robert D, Pouget J, Giovanni A, Azulay JP, Triglia JM (1999) Quantitative voice analysis in the assessment of bulbar involvement in amyotrophic lateral sclerosis. Acta Otolaryngol 119(6):724–731CrossRefPubMedGoogle Scholar
  7. 7.
    Konstantopoulos K, Christou YP, Vogazianos P, Zamba-Papanicolaou E, Kleopa KA (2017) A quantitative method for the assessment of dysarthrophonia in myasthenia gravis. J Neurol Sci 377:42–46. CrossRefPubMedGoogle Scholar
  8. 8.
    Fourcin AJ. Laryngographic assessment of phonatory function (1981). The American Speech-Language-Hearing Association, ASHA Reports: 116–127Google Scholar
  9. 9.
    Antonogeorgos G, Panagiotakos DB, Priftis KN, Tzonou A (2009). Logistic regression and linear discriminant analyses in evaluating factors associated with asthma prevalence among 10- to 12-years-old children: divergence and similarity of the two statistical methods. Int J Pediatr: 952042.
  10. 10.
    Natsios G, Pastaka C, Vavougios G, Zarogiannis SG, Tsolaki V, Dimoulis A, Seitanidis G, Gourgoulianis KI (2016) Age, body mass index, and daytime and nocturnal hypoxia as predictors of hypertension in patients with obstructive sleep apnea. J Clin Hypertens (Greenwich) 18(2):146–152. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of ThessalyLarissaGreece
  2. 2.Department of NeurologyAthens Naval HospitalAthensGreece
  3. 3.Health Sciences Department, Speech TherapyEuropean University CyprusNicosiaCyprus
  4. 4.Cyprus Institute for Neurology and GeneticsNicosiaCyprus

Personalised recommendations